OpenRaiser/Intern-Atlas huggingface dataset:
Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts
Venue: arXiv cs.CL 2025
Year: 2025

Sentiment classification in short text datasets faces significant challenges such as class imbalance, limited training samples, and the inherent subjectivity of sentiment labels -- issues that are further intensified by the limited context in short texts. These factors make it difficult to resolve ambiguity and exacerbate data sparsity, hindering effective learning. In this paper, we evaluate the effectiveness of small Transformer-based models (i.e., BERT and RoBERTa, with fewer than 1 billion parameters) for multi-label sentiment classification, with a particular focus on short-text settings. Specifically, we evaluated three key factors influencing model performance: (1) continued domain-specific pre-training, (2) data augmentation using automatically generated examples, specifically generative data augmentation, and (3) architectural variations of the classification head. Our experiment results show that data augmentation improves classification performance, while continued pre-training on augmented datasets can introduce noise rather than boost accuracy. Furthermore, we confirm that modifications to the classification head yield only marginal benefits. These findings provide practical guidance for optimizing BERT-based models in resource-constrained settings and refining strategies for sentiment classification in short-text datasets.
Dual-Domain Perspective on Degradation-Aware Fusion: A VLM-Guided Robust Infrared and Visible Image Fusion Framework
Venue: arXiv cs.CV 2025
Year: 2025

Most existing infrared-visible image fusion (IVIF) methods assume high-quality inputs, and therefore struggle to handle dual-source degraded scenarios, typically requiring manual selection and sequential application of multiple pre-enhancement steps. This decoupled pre-enhancement-to-fusion pipeline inevitably leads to error accumulation and performance degradation. To overcome these limitations, we propose Guided Dual-Domain Fusion (GD^2Fusion), a novel framework that synergistically integrates vision-language models (VLMs) for degradation perception with dual-domain (frequency/spatial) joint optimization. Concretely, the designed Guided Frequency Modality-Specific Extraction (GFMSE) module performs frequency-domain degradation perception and suppression and discriminatively extracts fusion-relevant sub-band features. Meanwhile, the Guided Spatial Modality-Aggregated Fusion (GSMAF) module carries out cross-modal degradation filtering and adaptive multi-source feature aggregation in the spatial domain to enhance modality complementarity and structural consistency. Extensive qualitative and quantitative experiments demonstrate that GD^2Fusion achieves superior fusion performance compared with existing algorithms and strategies in dual-source degraded scenarios. The code will be publicly released after acceptance of this paper.
Graph Reconstruction with a Connected Components Oracle
Venue: arXiv cs.DS 2025
Year: 2025

In the Graph Reconstruction (GR) problem, the goal is to recover a hidden graph by utilizing some oracle that provides limited access to the structure of the graph. The interest is in characterizing how strong different oracles are when the complexity of an algorithm is measured in the number of performed queries. We study a novel oracle that returns the set of connected components (CC) on the subgraph induced by the queried subset of vertices. Our main contributions are as follows: 1. For a hidden graph with $n$ vertices, $m$ edges, maximum degree $Δ$, and treewidth $k$, GR can be solved in $O(\min\{m / \log m, Δ^2, k^2\} \cdot \log n)$ CC queries by an adaptive randomized algorithm. 2. For a hidden graph with $n$ vertices and degeneracy $d$, GR can be solved in $O(d^2 \log^2 n)$ CC queries by an adaptive randomized algorithm. 3. For a hidden graph with $n$ vertices, $m$ edges, maximum degree $Δ$, and treewidth $k$, no algorithm can solve GR in $o(\min\{m, Δ^2, k^2\})$ CC queries.
Do Large Language Models Need Intent? Revisiting Response Generation Strategies for Service Assistant
Venue: arXiv cs.CL 2025
Year: 2025

In the era of conversational AI, generating accurate and contextually appropriate service responses remains a critical challenge. A central question remains: Is explicit intent recognition a prerequisite for generating high-quality service responses, or can models bypass this step and produce effective replies directly? This paper conducts a rigorous comparative study to address this fundamental design dilemma. Leveraging two publicly available service interaction datasets, we benchmark several state-of-the-art language models, including a fine-tuned T5 variant, across both paradigms: Intent-First Response Generation and Direct Response Generation. Evaluation metrics encompass both linguistic quality and task success rates, revealing surprising insights into the necessity or redundancy of explicit intent modelling. Our findings challenge conventional assumptions in conversational AI pipelines, offering actionable guidelines for designing more efficient and effective response generation systems.
On approximating the $f$-divergence between two Ising models
Venue: arXiv cs.DS 2025
Year: 2025

The $f$-divergence is a fundamental notion that measures the difference between two distributions. In this paper, we study the problem of approximating the $f$-divergence between two Ising models, which is a generalization of recent work on approximating the TV-distance. Given two Ising models $ν$ and $μ$, which are specified by their interaction matrices and external fields, the problem is to approximate the $f$-divergence $D_f(ν\,\|\,μ)$ within an arbitrary relative error $\mathrm{e}^{\pm \varepsilon}$. For $χ^α$-divergence with a constant integer $α$, we establish both algorithmic and hardness results. The algorithm works in a parameter regime that matches the hardness result. Our algorithm can be extended to other $f$-divergences such as $α$-divergence, Kullback-Leibler divergence, Rényi divergence, Jensen-Shannon divergence, and squared Hellinger distance.
Leveraging Transfer Learning and Mobile-enabled Convolutional Neural Networks for Improved Arabic Handwritten Character Recognition
Venue: arXiv cs.CV 2025
Year: 2025

The study explores the integration of transfer learning (TL) with mobile-enabled convolutional neural networks (MbNets) to enhance Arabic Handwritten Character Recognition (AHCR). Addressing challenges like extensive computational requirements and dataset scarcity, this research evaluates three TL strategies--full fine-tuning, partial fine-tuning, and training from scratch--using four lightweight MbNets: MobileNet, SqueezeNet, MnasNet, and ShuffleNet. Experiments were conducted on three benchmark datasets: AHCD, HIJJA, and IFHCDB. MobileNet emerged as the top-performing model, consistently achieving superior accuracy, robustness, and efficiency, with ShuffleNet excelling in generalization, particularly under full fine-tuning. The IFHCDB dataset yielded the highest results, with 99% accuracy using MnasNet under full fine-tuning, highlighting its suitability for robust character recognition. The AHCD dataset achieved competitive accuracy (97%) with ShuffleNet, while HIJJA posed significant challenges due to its variability, achieving a peak accuracy of 92% with ShuffleNet. Notably, full fine-tuning demonstrated the best overall performance, balancing accuracy and convergence speed, while partial fine-tuning underperformed across metrics. These findings underscore the potential of combining TL and MbNets for resource-efficient AHCR, paving the way for further optimizations and broader applications. Future work will explore architectural modifications, in-depth dataset feature analysis, data augmentation, and advanced sensitivity analysis to enhance model robustness and generalizability.
Capturing an Invisible Robber using Separators
Venue: arXiv cs.DM 2025
Year: 2025

We study the zero-visibility cops and robbers game, where the robber is invisible to the cops until they are caught. This differs from the classic game where full information about the robber's location is known at any time. A previously known solution for capturing a robber in the zero-visibility case is based on the pathwidth decomposition. We provide an alternative solution based on a separation hierarchy, improving capture time and space complexity without asymptotically increasing the zero-visibility cop number in most cases. In addition, we provide a better bound on the approximate zero-visibility cop number for various classes of graphs, where approximate refers to the restriction to polynomial time computable strategies.
ModalSurv: Investigating opportunities and limitations of multimodal deep survival learning in prostate and bladder cancer
Venue: arXiv cs.LG 2025
Year: 2025

Accurate survival prediction is essential for personalised cancer treatment. We propose ModalSurv, a multimodal deep survival framework integrating clinical, MRI, histopathology, and RNA-sequencing data via modality-specific projections and cross-attention fusion. On the CHIMERA Grand Challenge datasets, ModalSurv achieved a C-index of 0.7402 (1st) for prostate and 0.5740 (5th) for bladder cancer. Notably, clinical features alone outperformed multimodal models on external tests, highlighting challenges of limited multimodal alignment and potential overfitting. Local validation showed multimodal gains but limited generalisation. ModalSurv provides a systematic evaluation of multimodal survival modelling, underscoring both its promise and current limitations for scalable, generalisable cancer prognosis.
Long-Term Experiences From Working with Extended Reality in the Wild
Venue: arXiv cs.HC 2025
Year: 2025

Extended Reality (XR) is increasingly used as a productivity tool and recent commercial XR devices have even been specifically designed as productivity tools, or, at least, are heavily advertised for such purposes, such as the Apple Vision Pro (AVP), which has now been available for more than one year. In spite of what marketing suggests, research still lacks an understanding of the long-term usage of such devices in ecologically valid everyday settings, as most studies are conducted in very controlled environments. Therefore, we conducted interviews with ten AVP users to better understand how experienced users engage with the device, and which limitations persist. Our participants report that XR can increase productivity and that they got used to the device after some time. Yet, a range of limitations persist that might hinder the widespread use of XR as a productivity tool, such as a lack of native applications, difficulties when integrating XR into current workflows, and limited possibilities to adapt and customize the XR experience.
GeoSplat: A Deep Dive into Geometry-Constrained Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

A few recent works explored incorporating geometric priors to regularize the optimization of Gaussian splatting, further improving its performance. However, those early studies mainly focused on the use of low-order geometric priors (e.g., normal vector), and they might also be unreliably estimated by noise-sensitive methods, like local principal component analysis. To address their limitations, we first present GeoSplat, a general geometry-constrained optimization framework that exploits both first-order and second-order geometric quantities to improve the entire training pipeline of Gaussian splatting, including Gaussian initialization, gradient update, and densification. As an example, we initialize the scales of 3D Gaussian primitives in terms of principal curvatures, leading to a better coverage of the object surface than random initialization. Secondly, based on certain geometric structures (e.g., local manifold), we introduce efficient and noise-robust estimation methods that provide dynamic geometric priors for our framework. We conduct extensive experiments on multiple datasets for novel view synthesis, showing that our framework, GeoSplat, significantly improves the performance of Gaussian splatting and outperforms previous baselines.
Recurrent State Encoders for Efficient Neural Combinatorial Optimization
Venue: arXiv cs.LG 2025
Year: 2025

The primary paradigm in Neural Combinatorial Optimization (NCO) are construction methods, where a neural network is trained to sequentially add one solution component at a time until a complete solution is constructed. We observe that the typical changes to the state between two steps are small, since usually only the node that gets added to the solution is removed from the state. An efficient model should be able to reuse computation done in prior steps. To that end, we propose to train a recurrent encoder that computes the state embeddings not only based on the state but also the embeddings of the step before. We show that the recurrent encoder can achieve equivalent or better performance than a non-recurrent encoder even if it consists of $3\times$ fewer layers, thus significantly improving on latency. We demonstrate our findings on three different problems: the Traveling Salesman Problem (TSP), the Capacitated Vehicle Routing Problem (CVRP), and the Orienteering Problem (OP) and integrate the models into a large neighborhood search algorithm, to showcase the practical relevance of our findings.
Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers
Venue: arXiv cs.CV 2025
Year: 2025

Robustifying convolutional neural networks (CNNs) against adversarial attacks remains challenging and often requires resource-intensive countermeasures. We explore the use of sparse mixture-of-experts (MoE) layers to improve robustness by replacing selected residual blocks or convolutional layers, thereby increasing model capacity without additional inference cost. On ResNet architectures trained on CIFAR-100, we find that inserting a single MoE layer in the deeper stages leads to consistent improvements in robustness under PGD and AutoPGD attacks when combined with adversarial training. Furthermore, we discover that when switch loss is used for balancing, it causes routing to collapse onto a small set of overused experts, thereby concentrating adversarial training on these paths and inadvertently making them more robust. As a result, some individual experts outperform the gated MoE model in robustness, suggesting that robust subpaths emerge through specialization. Our code is available at https://github.com/KASTEL-MobilityLab/robust-sparse-moes.
ProToM: Promoting Prosocial Behaviour via Theory of Mind-Informed Feedback
Venue: arXiv cs.AI 2025
Year: 2025

While humans are inherently social creatures, the challenge of identifying when and how to assist and collaborate with others - particularly when pursuing independent goals - can hinder cooperation. To address this challenge, we aim to develop an AI system that provides useful feedback to promote prosocial behaviour - actions that benefit others, even when not directly aligned with one's own goals. We introduce ProToM, a Theory of Mind-informed facilitator that promotes prosocial actions in multi-agent systems by providing targeted, context-sensitive feedback to individual agents. ProToM first infers agents' goals using Bayesian inverse planning, then selects feedback to communicate by maximising expected utility, conditioned on the inferred goal distribution. We evaluate our approach against baselines in two multi-agent environments: Doors, Keys, and Gems, as well as Overcooked. Our results suggest that state-of-the-art large language and reasoning models fall short of communicating feedback that is both contextually grounded and well-timed - leading to higher communication overhead and task speedup. In contrast, ProToM provides targeted and helpful feedback, achieving a higher success rate, shorter task completion times, and is consistently preferred by human users.
Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning has the potential to unlock siloed data and distributed resources by enabling collaborative model training without sharing private data. As more complex foundational models gain widespread use, the need to expand training resources and integrate privately owned data grows as well. In this article, we explore the intersection of federated learning and foundational models, aiming to identify, categorize, and characterize technical methods that integrate the two paradigms. As a unified survey is currently unavailable, we present a literature survey structured around a novel taxonomy that follows the development life-cycle stages, along with a technical comparison of available methods. Additionally, we provide practical insights and guidelines for implementing and evolving these methods, with a specific focus on the healthcare domain as a case study, where the potential impact of federated learning and foundational models is considered significant. Our survey covers multiple intersecting topics, including but not limited to federated learning, self-supervised learning, fine-tuning, distillation, and transfer learning. Initially, we retrieved and reviewed a set of over 4,200 articles. This collection was narrowed to more than 250 thoroughly reviewed articles through inclusion criteria, featuring 42 unique methods. The methods were used to construct the taxonomy and enabled their comparison based on complexity, efficiency, and scalability. We present these results as a self-contained overview that not only summarizes the state of the field but also provides insights into the practical aspects of adopting, evolving, and integrating foundational models with federated learning.
Efficient Contractions of Dynamic Graphs -- with Applications
Venue: arXiv cs.DS 2025
Year: 2025

A non-trivial minimum cut (NMC) sparsifier is a multigraph $\hat{G}$ that preserves all non-trivial minimum cuts of a given undirected graph $G$. We introduce a flexible data structure for fully dynamic graphs that can efficiently provide an NMC sparsifier upon request at any point during the sequence of updates. We employ simple dynamic forest data structures to achieve a fast from-scratch construction of the sparsifier at query time. Based on the strength of the adversary and desired type of time bounds, the data structure comes with different guarantees. Specifically, let $G$ be a fully dynamic simple graph with $n$ vertices and minimum degree $δ$. Then our data structure supports an insertion/deletion of an edge to/from $G$ in $n^{o(1)}$ worst-case time. Furthermore, upon request, it can return w.h.p. an NMC sparsifier of $G$ that has $O(n/δ)$ vertices and $O(n)$ edges, in $\hat{O}(n)$ time. The probabilistic guarantees hold against an adaptive adversary. Alternatively, the update and query times can be improved to $\tilde{O}(1)$ and $\tilde{O}(n)$ respectively, if amortized-time guarantees are sufficient, or if the adversary is oblivious. We discuss two applications of our data structure. First, it can be used to efficiently report a cactus representation of all minimum cuts of a fully dynamic simple graph. Using the NMC sparsifier we can w.h.p. build this cactus in worst-case time $\hat{O}(n)$ against an adaptive adversary. Second, our data structure allows us to efficiently compute the maximal $k$-edge-connected subgraphs of undirected simple graphs, by repeatedly applying a minimum cut algorithm on the NMC sparsifier. Specifically, we can compute w.h.p. the maximal $k$-edge-connected subgraphs of a simple graph with $n$ vertices and $m$ edges in $\tilde{O}(m+n^2/k)$ time which is an improvement for $k = Ω(n^{1/8})$ and works for fully dynamic graphs.
Jamming Smarter, Not Harder: Exploiting O-RAN Y1 RAN Analytics for Efficient Interference
Venue: arXiv cs.CR 2025
Year: 2025

The Y1 interface in O-RAN enables the sharing of RAN Analytics Information (RAI) between the near-RT RIC and authorized Y1 consumers, which may be internal applications within the operator's trusted domain or external systems accessing data through a secure exposure function. While this visibility enhances network optimization and enables advanced services, it also introduces a potential security risk -- a malicious or compromised Y1 consumer could misuse analytics to facilitate targeted interference. In this work, we demonstrate how an adversary can exploit the Y1 interface to launch selective jamming attacks by passively monitoring downlink metrics. We propose and evaluate two Y1-aided jamming strategies: a clustering-based jammer leveraging DBSCAN for traffic profiling and a threshold-based jammer. These are compared against two baselines strategies -- always-on jammer and random jammer -- on an over-the-air LTE/5G O-RAN testbed. Experimental results show that in unconstrained jamming budget scenarios, the threshold-based jammer can closely replicate the disruption caused by always-on jamming while reducing transmission time by 27\%. Under constrained jamming budgets, the clustering-based jammer proves most effective, causing up to an 18.1\% bitrate drop while remaining active only 25\% of the time. These findings reveal a critical trade-off between jamming stealthiness and efficiency, and illustrate how exposure of RAN analytics via the Y1 interface can enable highly targeted, low-overhead attacks, raising important security considerations for both civilian and mission-critical O-RAN deployments.
Verifiability and Privacy in Federated Learning through Context-Hiding Multi-Key Homomorphic Authenticators
Venue: arXiv cs.CR 2025
Year: 2025

Federated Learning has rapidly expanded from its original inception to now have a large body of research, several frameworks, and sold in a variety of commercial offerings. Thus, its security and robustness is of significant importance. There are many algorithms that provide robustness in the case of malicious clients. However, the aggregator itself may behave maliciously, for example, by biasing the model or tampering with the weights to weaken the models privacy. In this work, we introduce a verifiable federated learning protocol that enables clients to verify the correctness of the aggregators computation without compromising the confidentiality of their updates. Our protocol uses a standard secure aggregation technique to protect individual model updates with a linearly homomorphic authenticator scheme that enables efficient, privacy-preserving verification of the aggregated result. Our construction ensures that clients can detect manipulation by the aggregator while maintaining low computational overhead. We demonstrate that our approach scales to large models, enabling verification over large neural networks with millions of parameters.
Accuracy-Constrained CNN Pruning for Efficient and Reliable EEG-Based Seizure Detection
Venue: arXiv cs.LG 2025
Year: 2025

Deep learning models, especially convolutional neural networks (CNNs), have shown considerable promise for biomedical signals such as EEG-based seizure detection. However, these models come with challenges, primarily due to their size and compute requirements in environments where real-time detection or limited resources are available. In this study, we present a lightweight one-dimensional CNN model with structured pruning to improve efficiency and reliability. The model was trained with mild early stopping to address possible overfitting, achieving an accuracy of 92.78% and a macro-F1 score of 0.8686. Structured pruning of the baseline CNN involved removing 50% of the convolutional kernels based on their importance to model predictions. Surprisingly, after pruning the weights and memory by 50%, the new network was still able to maintain predictive capabilities, while modestly increasing precision to 92.87% and improving the macro-F1 score to 0.8707. Overall, we present a convincing case that structured pruning removes redundancy, improves generalization, and, in combination with mild early stopping, achieves a promising way forward to improve seizure detection efficiency and reliability, which is clear motivation for resource-limited settings.